The relationship of fracture toughness coefficients and geophysical characteristics of rocks of hydrocarbon deposits

Кашников, Ю А; Ashikhmin, S. G.; Kukhtinskii, A. E.; Shustov, D. V.

doi:10.31897/pmi.2020.1.83

Cited by 14 publications

(18 citation statements)

References 1 publication

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“…In the past few decades, various advanced computational approaches, e.g., finite element, numerical linear algebra, statistics, numerical analysis, tensor analysis, and artificial intelligence, have been applied in various fields of study, such as fluid mechanic engineering [26][27][28][29][30][31][32][33][34], chemical engineering [35][36][37][38][39][40][41], electrical and computer engineering , petrochemical engineering [73][74][75][76][77], petroleum engineering [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92], mathematics and physics [93][94][95][96][97][98][99][100][101][102], and environmental engineering [103]…”

Section: Mlp Neural Networkmentioning

confidence: 99%

Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

et al. 2022

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One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.

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Section: Mlp Neural Networkmentioning

confidence: 99%

Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

et al. 2022

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show abstract

“…In the past few decades, various advanced computational methods have been applied in various fields of study such as chemical engineering [32][33][34][35][36][37], electrical and computer engineering [38][39][40][41], civil engineering [42][43][44], mechanical engineering [45][46][47][48][49][50][51], petroleum engineering [52][53][54][55][56][57][58][59][60][61][62][63], and environmental engineering [64,65], etc. The ANN has been demonstrated to be the most potent technique for classification and prediction among the aforementioned computational methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

et al. 2022

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In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

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“…The composition, structure, and properties of these deposits must be considered when choosing the most efficient method for removing ARPDs [12,14,[30][31][32][33]. The composition of organic sediments is determined by their origin, the composition of the oil, and the mechanism of their formation.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Efficiency of Oil and Gas Wells Complicated by the Formation of Asphalt–Resin–Paraffin Deposits

2021

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A number of difficulties may be encountered in the final stages of oil field exploitation, including the formation of asphalt–resin–paraffin deposits (ARPDs). It is expedient to use complex technologies to remove the already formed deposits and prevent the formation of ARPDs. This paper focuses on the complex technology of oil field exploitation. This technology combines both the removal of organic deposits and the prevention of the formation of these deposits in the well bottomhole formation zone (BHFZ) system. The calculations for determining the process parameters of selling the ARPD inhibitor solution into the BHFZ are presented in this article. This complex technology includes the process of ARPD removal by flushing the well and the subsequent injection of the developed ARPD solvent into the BHFZ. In addition, the technology is complemented by a method of preventing the formation of these deposits. This method consists of squeezing the ARPD inhibitor and then pumping it by the selling fluid from five to ten times of the volume. This article contains a detailed calculation of the methodology and provides the diagrams for the solvent and inhibitor injection.

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The relationship of fracture toughness coefficients and geophysical characteristics of rocks of hydrocarbon deposits

Cited by 14 publications

References 1 publication

Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

Improving the Efficiency of Oil and Gas Wells Complicated by the Formation of Asphalt–Resin–Paraffin Deposits

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